Natural language understanding systems require a knowledge base provided with conceptual representations reflecting the structure of human beings’ cognitive system. Although surface semantics can be sufficient in some other systems, the construction of a robust knowledge base guarantees its use in most natural language processing applications, consolidating thus the concept of resource reuse. In this scenario, FunGramKB is presented as a multipurpose knowledge base whose model has been particularly designed for natural language understanding tasks. The theoretical basement of this knowledge engineering project lies in the construction of two complementary types of interlingua: the conceptual logical structure, i.e. a lexically-driven interlingua which can predict linguistic phenomena according to the Role and Reference Grammar syntax-semantics interface, and the COREL scheme, i.e. a concept-oriented interlingua on which our rule-based reasoning engine is able to make inferences effectively. The objective of the paper is to describe the different conceptual, lexical and grammatical modules which make up the architecture of FunGramKB, together with an exploratory outline on how to exploit such a knowledge base within an NLP system.